A recent ITiCSE working group investigated when and how experts give feedback and hints at steps novice programmers take when solving programming problems. Based on the feedback literature and an analysis of expert feedback on steps, the working group designed guidelines for when and how to give feedback. The feedback provided by educators using these guidelines on a number of sequences of student steps varied a lot. In this paper, we try to answer the question of why educators give feedback at particular steps to novice learners of programming. We prepared six authentic sequences of student steps when solving an introductory programming task. The preprocessed sequences were used in a survey to gather information about when and why an expert would give feedback. Respondents annotated each step from one sequence with if and why they would give feedback at that step. Our survey received 47 responses. We qualitatively analyzed the responses, resulting in a coding scheme consisting of 19 different reasons for why experts intervene (or not) when novice learners work on introductory programming tasks. We found a considerable variety of reasons experts give for when and how to help students with feedback and hints. Also, sometimes one expert uses a reason at a step to explain why they do intervene, and another expert uses the same reason at the step to not intervene. The categories of experts' feedback indicators will pave the way for several future studies and applications, including learning systems trying to resemble expert feedback strategies.

Ever since the emergence of large language models (LLMs) and related applications, such as ChatGPT, its performance and error analysis for programming tasks have been subject to research. In this work-in-progress paper, we explore the potential of such LLMs for computing educators and learners, as we analyze the feedback it generates to a given input containing program code. In particular, we aim at (1) exploring how an LLM like ChatGPT responds to students seeking help with their introductory programming tasks, and (2) identifying feedback types in its responses. To achieve these goals, we used students' programming sequences from a dataset gathered within a CS1 course as input for ChatGPT along with questions required to elicit feedback and correct solutions. The results show that ChatGPT performs reasonably well for some of the introductory programming tasks and student errors, which means that students can potentially benefit. However, educators should provide guidance on how to use the provided feedback, as it can contain misleading information for novices.

To cater to the increasingly diverse student bodies, higher education has to personalize education. In times of stagnant educational budgets and staffing problems, this can only be achieved via adaptive, interactive learning support services. In this paper we show how these can be generated by modeling the domain, the learner competencies, and the rhetoric and didactic relations among learning objects, re-using existing technologies and systems of mathematical knowledge management.

Every year, millions of students learn how to write programs. Learning activities for beginners almost always include programming tasks that require a student to write a program to solve a particular problem. When learning how to solve such a task, many students need feedback on their previous actions, and hints on how to proceed. In the case of programming, the feedback should take the steps a student has taken towards implementing a solution into account, and the hints should help a student to complete or improve a possibly partial solution. Only a limited number of learning environments for programming give feedback and hints on intermediate steps students take towards a solution, and little is known about the quality of the feedback provided. To determine the quality of feedback of such tools and to help further developing them, we create and curate data sets that show what kinds of steps students take when solving programming exercises for beginners, and what kind of feedback and hints should be provided. This working group aims to 1) select or create several data sets with steps students take to solve programming tasks, 2) introduce a method to annotate students' steps in these data sets, 3) attach feedback and hints to these steps, 4) set up a method to utilize these data sets in various learning environments for programming, and 5) analyse the quality of hints and feedback in these learning environments.